{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## RNN——CIFAR-10"
   ],
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   },
   "id": "a6102839fbf8945a"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "initial_id",
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    "ExecuteTime": {
     "end_time": "2025-06-12T02:06:25.956622Z",
     "start_time": "2025-06-12T02:06:25.950149Z"
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   },
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   "source": [
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据集路径\n",
    "cifar10_data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'\n",
    "\n",
    "# 自定义CIFAR-10数据集加载函数\n",
    "def load_cifar10(data_dir):\n",
    "    def unpickle(file):\n",
    "        with open(file, 'rb') as fo:\n",
    "            dict = pickle.load(fo, encoding='bytes')\n",
    "        return dict\n",
    "\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_file = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        batch = unpickle(batch_file)\n",
    "        train_data.append(batch[b'data'])\n",
    "        train_labels.extend(batch[b'labels'])\n",
    "    train_data = np.vstack(train_data).reshape(-1, 3, 32, 32)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_file = os.path.join(data_dir, 'test_batch')\n",
    "    test_batch = unpickle(test_file)\n",
    "    test_data = np.array(test_batch[b'data']).reshape(-1, 3, 32, 32)\n",
    "    test_labels = np.array(test_batch[b'labels'])\n",
    "\n",
    "    # 归一化数据\n",
    "    train_data = train_data / 255.0\n",
    "    test_data = test_data / 255.0\n",
    "\n",
    "    return train_data, test_data, train_labels, test_labels\n",
    "\n",
    "# 自定义CIFAR-10数据集类\n",
    "class CustomCIFAR10Dataset(Dataset):\n",
    "    classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "\n",
    "    def __init__(self, images, labels):\n",
    "        self.images = images\n",
    "        self.labels = labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image = self.images[idx]\n",
    "        label = self.labels[idx]\n",
    "        image = torch.from_numpy(image).float()  # 转换为PyTorch张量\n",
    "        label = torch.from_numpy(np.array(label)).long()  # 确保标签是torch.long类型\n",
    "        return image, label\n",
    "\n",
    "# 加载数据集\n",
    "train_data, test_data, train_labels, test_labels = load_cifar10(cifar10_data_dir)\n",
    "\n",
    "# 创建自定义数据集\n",
    "train_dataset = CustomCIFAR10Dataset(train_data, train_labels)\n",
    "test_dataset = CustomCIFAR10Dataset(test_data, test_labels)\n",
    "\n",
    "# 创建数据加载器\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, drop_last=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-12T02:06:27.507493Z",
     "start_time": "2025-06-12T02:06:26.428998Z"
    }
   },
   "id": "6da23bf10a40c614",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 1.9911\n",
      "Epoch [2/10], Loss: 1.9070\n",
      "Epoch [3/10], Loss: 1.8454\n",
      "Epoch [4/10], Loss: 1.7893\n",
      "Epoch [5/10], Loss: 1.8024\n",
      "Epoch [6/10], Loss: 1.7214\n",
      "Epoch [7/10], Loss: 1.7065\n",
      "Epoch [8/10], Loss: 1.7845\n",
      "Epoch [9/10], Loss: 1.7211\n",
      "Epoch [10/10], Loss: 1.7649\n",
      "Training Time: 33.36 seconds\n",
      "Test Accuracy: 0.3510\n",
      "Inference Time: 0.42 seconds\n"
     ]
    }
   ],
   "source": [
    "# 定义适用于CIFAR-10的RNN模型\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
    "        super(RNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 确保输入数据的形状为 (batch_size, sequence_length, input_size)\n",
    "        # 初始化隐藏状态\n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n",
    "        # 前向传播 RNN\n",
    "        out, _ = self.rnn(x, h0)\n",
    "        # 取最后一个时间步的输出\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out\n",
    "\n",
    "# 模型参数\n",
    "input_size = 32 * 3  # CIFAR-10 图像通道数\n",
    "sequence_length = 32  # CIFAR-10 图像高度\n",
    "hidden_size = 128\n",
    "num_layers = 2\n",
    "num_classes = 10\n",
    "\n",
    "# 初始化模型、损失函数和优化器\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 10\n",
    "start_time = time.time()\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    epoch_loss = 0.0\n",
    "    for images, labels in train_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        images = images.permute(0, 2, 3, 1)  # 变为 [64, 32, 32, 3]\n",
    "        images = images.reshape(images.size(0), images.size(1), -1)  # 变为 [64, 32, 96]\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        epoch_loss += loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss / len(train_loader):.4f}')\n",
    "\n",
    "train_time = time.time() - start_time\n",
    "print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "\n",
    "# 测试模型\n",
    "model.eval()\n",
    "start_inference = time.time()\n",
    "\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    all_labels = []\n",
    "    all_preds = []\n",
    "    for images, labels in test_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # 与训练相同的处理方式\n",
    "        images = images.permute(0, 2, 3, 1)  # 变为 [64, 32, 32, 3]\n",
    "        images = images.reshape(images.size(0), images.size(1), -1)  # 变为 [64, 32, 96]\n",
    "        \n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "    test_acc = correct / total\n",
    "    print(f\"Test Accuracy: {test_acc:.4f}\")\n",
    "\n",
    "inference_time = time.time() - start_inference\n",
    "print(f\"Inference Time: {inference_time:.2f} seconds\")"
   ],
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    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-12T02:07:01.862803Z",
     "start_time": "2025-06-12T02:06:28.066699Z"
    }
   },
   "id": "72fbfa94eb925774",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance Metrics:\n",
      "Accuracy: 0.3510\n",
      "Precision: 0.3656\n",
      "Recall: 0.3510\n",
      "F1-score: 0.3360\n",
      "Training Time: 33.36 seconds\n",
      "Inference Time: 0.42 seconds\n",
      "Confusion Matrix:\n",
      " [[340  49  44  32  13  38  51 172 118 141]\n",
      " [ 42 409   3  18   2   3  39  35  58 390]\n",
      " [ 40  11 113  74  79 105 184 280  36  77]\n",
      " [  7   7  38 109  29 260 233 157  33 124]\n",
      " [ 22   2  81  52 164  65 212 326  24  52]\n",
      " [  4   4  55  81  61 350 179 176  28  59]\n",
      " [  1   9  23  54  78  64 498 141  19 113]\n",
      " [ 12   3  32  39  51  64 151 548  14  83]\n",
      " [111 118  11  27   5  60  33  51 398 183]\n",
      " [ 35 202   1  23   4   8  56  51  45 575]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.34      0.42       998\n",
      "         car       0.50      0.41      0.45       999\n",
      "        bird       0.28      0.11      0.16       999\n",
      "         cat       0.21      0.11      0.14       997\n",
      "        deer       0.34      0.16      0.22      1000\n",
      "         dog       0.34      0.35      0.35       997\n",
      "        frog       0.30      0.50      0.38      1000\n",
      "       horse       0.28      0.55      0.37       997\n",
      "        ship       0.51      0.40      0.45       997\n",
      "       truck       0.32      0.57      0.41      1000\n",
      "\n",
      "    accuracy                           0.35      9984\n",
      "   macro avg       0.37      0.35      0.34      9984\n",
      "weighted avg       0.37      0.35      0.34      9984\n"
     ]
    }
   ],
   "source": [
    "# 计算评估指标\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='weighted')\n",
    "conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "class_report = classification_report(all_labels, all_preds, target_names=CustomCIFAR10Dataset.classes)\n",
    "\n",
    "# 输出结果\n",
    "print(\"Performance Metrics:\")\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1-score: {f1:.4f}\")\n",
    "print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "print(f\"Inference Time: {inference_time:.2f} seconds\")\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
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    "ExecuteTime": {
     "end_time": "2025-06-12T02:08:57.484693Z",
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   "id": "98cec70bf373c891",
   "execution_count": 10
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    {
     "data": {
      "text/plain": "<Figure size 1200x1000 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"RNN - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
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     "end_time": "2025-06-12T02:09:09.992526Z",
     "start_time": "2025-06-12T02:09:09.250140Z"
    }
   },
   "id": "336e67641be84929",
   "execution_count": 11
  },
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   "cell_type": "code",
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   },
   "id": "6af99b4095ddec39"
  }
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